Monday, May 07, 2007

As a small team, we had to make choices that are reflected in our choice of hardware and algorithm. One of the most time intensive aspect of devising a robot is the development of an accurate motion model and then an adequate sensor/perceptual model [1].

We could not spend an entire team and the time to devise a motor model as well as a sensor model as what other teams have done. Some of it stems from the fact that we are not interested in getting M.S. theses written for the moment as it is the case here. Furthermore, our vehicle is far from being protected from the elements. And we considered that it would become difficult to rely on a model that could become less and less accurate as the car was getting older. Another aspect of our decision making came from the actuators we used. In the case of the steering wheel, there are moments when the steering motor can slip (especially in large deviations). This is owed to two factors: the laptop is busy doing other tasks can sometime send only the steering task with a delay or there is too much torque too be applied too fast. This results in a command control that would be difficult to model. Finally, we were interested in developing a robot that would learn it's own behavior while being supervised with very little labeled data. In other words, we want the learning to be based on data coming from an actual driving behavior. Not much data understanding should come from any subsequent data processing.

In the latter case, we decided against using the SDE approach but use Compressed Sensing instead for the dimensionality reduction aspect of the problem for building the sensor model. Some aspect of the reduction include some of the ideas in [7].

[3] [pdf][ps.gz]Subjective mapping, Michael Bowling, Dana Wilkinson, and Ali Ghodsi.In New Scientific and Technical Advances in Research (NECTAR) of the Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI) , pages 1569--1572, 2006.